Applied Data Science: Data Translators Across the Disciplines (Springer, Interdisciplinary Applied Sciences)
Douglas Woolford (Western University, email@example.com), Donna Kotsopoulos (Western University, firstname.lastname@example.org), and Boba Samuels (University of Toronto, email@example.com)
Contact & Submission Email
How can individuals who may not have a traditional data science background become data translators? People with data literacy proficiency – those identified as “data translators” (McKinsey Global Institute, 2016) – are in high demand. In organizations of all sizes and scopes, there has been an explosion in the need to access ever more complex data sets and growing engagement in data analytics. Of concern, however, is that much data instruction focuses on statistical, computing and other technical competencies. What has received less attention is communication and knowledge translation, using data, and across disciplines. While many users of data may not be considered “data scientists,” they nevertheless are required to translate data to address disciplinary problems and communicate data-driven solutions effectively for specific audiences. By presenting discipline-specific examples of data solutions by both data scientist and non-data scientists, we aim to illustrate effective data translation in practice.
We are interested in contributions that focus on effective data application and communication while simultaneously highlighting the process of producing an effective data science solution. We welcome submissions that advance a broad approach to developing cross-disciplinary data translators across a variety of fields, such as education, health sciences, natural sciences, politics, economics, business and management studies, sociology, and others.
Contributions are sought from authors who may:
- present case studies of how data is used and translated in various disciplinary contexts
- share their data science solution lifecycle to illustrate disciplinary constraints and affordances, e.g., using the scientific method of inquiry–namely hypothesis, design, collecting data, analysing data, and reporting results
- explain not only how data is used within a discipline, but also the way in which data is translated to inform the discipline
- discuss key considerations that inform their data science solution, such as: data carpentry, visualization and exploratory data analysis, as well as the iterative data modelling process with an emphasis on reproducible research (aka “open science”)
- share approaches to communicating results and interpretations, emphasizing knowledge translation
- present pedagogical approaches to developing effective data translators
Each chapter will focus on a specific discipline to support enhanced pedagogical approaches for communicating about data, especially written communication about large data. Each chapter should include description of a data science solution lifecycle as well as discussion of disciplinary conventions. Chapters will be 3500-4000 words in length, including references and notes.
The audience for this collection is professors and teachers across multiple disciplines and fields who teach students how to use data to answer disciplinary problems. It will be of interest especially to those teaching courses for non-data science majors. We hope to encourage cross-disciplinary sharing to inform pedagogies in education, health sciences, natural sciences, politics, economics, business and management studies, sociology, and others. This collection will also be of interest to scholars in higher education pedagogy and in writing studies.
We have a publication agreement with Springer for this manuscript, with a submission target of December 2021 and publication in 2022.
- 8 January 2021 Call for Proposals distributed
- 1 March 2021 Chapter Proposals due
- 29 March 2021 Authors notified of decisions
- 26 July 2021 Chapters due
- 17 September 2021 Chapters returned to authors for revision after peer review
- 25 October 2021 Final revised chapters due from authors
- 6 December 2021 Manuscript revisions completed and submitted to publisher
Interested scholars are encouraged to email a submission by the proposal due date of March 1, 2021 to firstname.lastname@example.org. Submissions must be in a Microsoft Word file and include a Chapter Proposal (see below) and a brief (one paragraph) biography of all authors. Please identify the corresponding author for multi-authored submissions.
All submissions will be peer-reviewed prior to acceptance. We will contact the corresponding author of all submissions by the notification date of March 29, 2021 with details regarding acceptance and next steps.
Chapter Proposal Requirements
A brief chapter proposal of 300-500 words (excluding references) should make clear how the proposed chapter responds to the call and advances the goals of this collection.
About the Editors
Douglas Woolford is an Associate Professor of Environmetrics in the Department of Statistical & Actuarial Sciences at the University of Western Ontario (Western), where he also is the Director of the Master of Data Analytics professional science master’s program. Much of his research focuses on the application and development of data science methodology to study wildland fire science and wildland fire management. He has co-led the development of a variety of data science and analytics curricula at Western. His research and his teaching constantly involves the communication of technical data analytics methods and applications to a diverse audience.
Donna Kotsopoulos is a professor and Dean of the Faculty of Education at the University of Western Ontario (Western). Donna has been extensively involved in knowledge mobilization activities and social innovation. Her most recently funded SSHRC grant focuses on storytelling with data (or data translation). Her work has been disseminated in premier academic journals and national and international conferences. Her work has also informed policy within the post-secondary sector. Her most extensive body of research explores learning and cognition in the area of mathematics education.
Boba Samuels directs the Health Sciences Writing Centre and is an Assistant Professor, Teaching Stream in the Faculty of Kinesiology and Physical Education (KPE) at the University of Toronto. She has published research examining writing assignments in university, faculty perceptions and approaches to writing instruction, and graduate student publishing. In 2018 she co-authored Mastering Academic Writing, which provides evidence-based writing instruction for advanced university students. She is currently working on a curriculum design project that explores online modules for writing instruction across the KPE undergraduate curriculum.